{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab23c813",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.4.0/tutorials/zh_cn/cv/mindspore_ssd.ipynb)&emsp;[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.4.0/tutorials/zh_cn/cv/mindspore_ssd.py)&emsp;[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/r2.4.0/tutorials/source_zh_cn/cv/ssd.ipynb)\n",
    "\n",
    "# SSD目标检测\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb3db9c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 模型简介\n",
    "\n",
    "SSD，全称Single Shot MultiBox Detector，是Wei Liu在ECCV 2016上提出的一种目标检测算法。使用Nvidia Titan X在VOC 2007测试集上，SSD对于输入尺寸300x300的网络，达到74.3%mAP(mean Average Precision)以及59FPS；对于512x512的网络，达到了76.9%mAP ，超越当时最强的Faster RCNN(73.2%mAP)。具体可参考论文<sup>[1]</sup>。\n",
    "SSD目标检测主流算法分成可以两个类型：\n",
    "\n",
    "1. two-stage方法：RCNN系列<br />\n",
    "\n",
    "    通过算法产生候选框，然后再对这些候选框进行分类和回归。<br />\n",
    "\n",
    "2. one-stage方法：YOLO和SSD<br />\n",
    "\n",
    "    直接通过主干网络给出类别位置信息，不需要区域生成。<br />\n",
    "\n",
    "SSD是单阶段的目标检测算法，通过卷积神经网络进行特征提取，取不同的特征层进行检测输出，所以SSD是一种多尺度的检测方法。在需要检测的特征层，直接使用一个3 $\\times$ 3卷积，进行通道的变换。SSD采用了anchor的策略，预设不同长宽比例的anchor，每一个输出特征层基于anchor预测多个检测框（4或者6）。采用了多尺度检测方法，浅层用于检测小目标，深层用于检测大目标。SSD的框架如下图：\n",
    "\n",
    "![SSD-1](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_1.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b982901",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 模型结构\n",
    "\n",
    "SSD采用VGG16作为基础模型，然后在VGG16的基础上新增了卷积层来获得更多的特征图以用于检测。SSD的网络结构如图所示。上面是SSD模型，下面是YOLO模型，可以明显看到SSD利用了多尺度的特征图做检测。\n",
    "\n",
    "![SSD-2](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_2.jpg)\n",
    "<br />\n",
    "\n",
    "两种单阶段目标检测算法的比较：<br />\n",
    "SSD先通过卷积不断进行特征提取，在需要检测物体的网络，直接通过一个3 $\\times$ 3卷积得到输出，卷积的通道数由anchor数量和类别数量决定，具体为(anchor数量*(类别数量+4))。  \n",
    "SSD对比了YOLO系列目标检测方法，不同的是SSD通过卷积得到最后的边界框，而YOLO对最后的输出采用全连接的形式得到一维向量，对向量进行拆解得到最终的检测框。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f176a899",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 模型特点\n",
    "\n",
    "- 多尺度检测\n",
    "\n",
    "    在SSD的网络结构图中我们可以看到，SSD使用了多个特征层，特征层的尺寸分别是38 $\\times$ 38，19 $\\times$ 19，10 $\\times$ 10，5 $\\times$ 5，3 $\\times$ 3，1 $\\times$ 1，一共6种不同的特征图尺寸。大尺度特征图（较靠前的特征图）可以用来检测小物体，而小尺度特征图（较靠后的特征图）用来检测大物体。多尺度检测的方式，可以使得检测更加充分（SSD属于密集检测），更能检测出小目标。\n",
    "\n",
    "- 采用卷积进行检测\n",
    "\n",
    "    与YOLO最后采用全连接层不同，SSD直接采用卷积对不同的特征图来进行提取检测结果。对于形状为m $\\times$ n $\\times$ p的特征图，只需要采用3 $\\times$ 3 $\\times$ p这样比较小的卷积核得到检测值。\n",
    "\n",
    "- 预设anchor\n",
    "\n",
    "    在YOLOv1中，直接由网络预测目标的尺寸，这种方式使得预测框的长宽比和尺寸没有限制，难以训练。在SSD中，采用预设边界框，我们习惯称它为anchor（在SSD论文中叫default bounding boxes），预测框的尺寸在anchor的指导下进行微调。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c469bbf",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 环境准备\n",
    "\n",
    "本案例基于MindSpore实现，开始实验前，请确保本地已经安装了mindspore、download、pycocotools、opencv-python。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff194502",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 数据准备与处理\n",
    "\n",
    "本案例所使用的数据集为COCO 2017。为了更加方便地保存和加载数据，本案例中在数据读取前首先将COCO数据集转换成MindRecord格式。使用MindSpore Record数据格式可以减少磁盘IO、网络IO开销，从而获得更好的使用体验和性能提升。\n",
    "首先我们需要下载处理好的MindRecord格式的COCO数据集。\n",
    "运行以下代码将数据集下载并解压到指定路径。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5eb64b68-a335-4f8f-8563-ade52b6692e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1000 training examples\n",
      "read 100 validation examples\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((32, 3, 256, 256), (32, 1, 5))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from mxnet import gluon, image, np, npx\n",
    "from d2l import mxnet as d2l\n",
    "from download import download\n",
    "\n",
    "npx.set_np()\n",
    "\n",
    "# dataset_url = \"https://d2l-data.s3-accelerate.amazonaws.com/banana-detection.zip\"\n",
    "# path = download(dataset_url, \"./\", kind=\"zip\", replace=True)\n",
    "\n",
    "#@save\n",
    "def read_data_bananas(is_train=True):\n",
    "    \"\"\"读取香蕉检测数据集中的图像和标签\"\"\"\n",
    "    data_dir = d2l.download_extract('banana-detection')\n",
    "    csv_fname = os.path.join(data_dir, 'bananas_train' if is_train\n",
    "                             else 'bananas_val', 'label.csv')\n",
    "    csv_data = pd.read_csv(csv_fname)\n",
    "    csv_data = csv_data.set_index('img_name')\n",
    "    images, targets = [], []\n",
    "    for img_name, target in csv_data.iterrows():\n",
    "        images.append(image.imread(\n",
    "            os.path.join(data_dir, 'bananas_train' if is_train else\n",
    "                         'bananas_val', 'images', f'{img_name}')))\n",
    "        # 这里的target包含（类别，左上角x，左上角y，右下角x，右下角y），\n",
    "        # 其中所有图像都具有相同的香蕉类（索引为0）\n",
    "        targets.append(list(target))\n",
    "    return images, np.expand_dims(np.array(targets), 1) / 256\n",
    "\n",
    "#@save\n",
    "class BananasDataset(gluon.data.Dataset):\n",
    "    \"\"\"一个用于加载香蕉检测数据集的自定义数据集\"\"\"\n",
    "    def __init__(self, is_train):\n",
    "        self.features, self.labels = read_data_bananas(is_train)\n",
    "        print('read ' + str(len(self.features)) + (f' training examples' if\n",
    "              is_train else f' validation examples'))\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return (self.features[idx].astype('float32').transpose(2, 0, 1),\n",
    "                self.labels[idx])\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)\n",
    "    \n",
    "def load_data_bananas(batch_size):\n",
    "    \"\"\"加载香蕉检测数据集\"\"\"\n",
    "    train_iter = gluon.data.DataLoader(BananasDataset(is_train=True),\n",
    "                                       batch_size, shuffle=True)\n",
    "    val_iter = gluon.data.DataLoader(BananasDataset(is_train=False),\n",
    "                                     batch_size)\n",
    "    return train_iter, val_iter\n",
    "\n",
    "batch_size, edge_size = 32, 256\n",
    "train_iter, _ = load_data_bananas(batch_size)\n",
    "batch = next(iter(train_iter))\n",
    "batch[0].shape, batch[1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1b5ac8",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "然后我们为数据处理定义一些输入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "849b1b9f-f563-4919-8c84-5a8991b21b68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 版本: 3.7.10 (default, Jun  4 2021, 14:48:32) \n",
      "[GCC 7.5.0]\n",
      "MindSpore 版本: 2.2.1\n"
     ]
    }
   ],
   "source": [
    "#运行installms221.pynb 切换 mindspore221kernel后继续\n",
    "import sys\n",
    "import mindspore\n",
    "\n",
    "# 输出 Python 版本\n",
    "print(f\"Python 版本: {sys.version}\")\n",
    "\n",
    "# 输出 MindSpore 版本\n",
    "print(f\"MindSpore 版本: {mindspore.__version__}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8292a570",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 数据采样\n",
    "\n",
    "为了使模型对于各种输入对象大小和形状更加鲁棒，SSD算法每个训练图像通过以下选项之一随机采样：\n",
    "\n",
    "- 使用整个原始输入图像\n",
    "\n",
    "- 采样一个区域，使采样区域和原始图片最小的交并比重叠为0.1,0.3,0.5,0.7或0.9\n",
    "\n",
    "- 随机采样一个区域\n",
    "\n",
    "每个采样区域的大小为原始图像大小的[0.3,1]，长宽比在1/2和2之间。如果真实标签框中心在采样区域内，则保留两者重叠部分作为新图片的真实标注框。在上述采样步骤之后，将每个采样区域大小调整为固定大小，并以0.5的概率水平翻转。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc75d5a8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def _rand(a=0., b=1.):\n",
    "    return np.random.rand() * (b - a) + a\n",
    "\n",
    "def intersect(box_a, box_b):\n",
    "    \"\"\"Compute the intersect of two sets of boxes.\"\"\"\n",
    "    max_yx = np.minimum(box_a[:, 2:4], box_b[2:4])\n",
    "    min_yx = np.maximum(box_a[:, :2], box_b[:2])\n",
    "    inter = np.clip((max_yx - min_yx), a_min=0, a_max=np.inf)\n",
    "    return inter[:, 0] * inter[:, 1]\n",
    "\n",
    "def jaccard_numpy(box_a, box_b):\n",
    "    \"\"\"Compute the jaccard overlap of two sets of boxes.\"\"\"\n",
    "    inter = intersect(box_a, box_b)\n",
    "    area_a = ((box_a[:, 2] - box_a[:, 0]) *\n",
    "              (box_a[:, 3] - box_a[:, 1]))\n",
    "    area_b = ((box_b[2] - box_b[0]) *\n",
    "              (box_b[3] - box_b[1]))\n",
    "    union = area_a + area_b - inter\n",
    "    return inter / union\n",
    "\n",
    "def random_sample_crop(image, boxes):\n",
    "    \"\"\"Crop images and boxes randomly.\"\"\"\n",
    "    height, width, _ = image.shape\n",
    "    min_iou = np.random.choice([None, 0.1, 0.3, 0.5, 0.7, 0.9])\n",
    "\n",
    "    if min_iou is None:\n",
    "        return image, boxes\n",
    "\n",
    "    for _ in range(50):\n",
    "        image_t = image\n",
    "        w = _rand(0.3, 1.0) * width\n",
    "        h = _rand(0.3, 1.0) * height\n",
    "        # aspect ratio constraint b/t .5 & 2\n",
    "        if h / w < 0.5 or h / w > 2:\n",
    "            continue\n",
    "\n",
    "        left = _rand() * (width - w)\n",
    "        top = _rand() * (height - h)\n",
    "        rect = np.array([int(top), int(left), int(top + h), int(left + w)])\n",
    "        overlap = jaccard_numpy(boxes, rect)\n",
    "\n",
    "        # dropout some boxes\n",
    "        drop_mask = overlap > 0\n",
    "        if not drop_mask.any():\n",
    "            continue\n",
    "\n",
    "        if overlap[drop_mask].min() < min_iou and overlap[drop_mask].max() > (min_iou + 0.2):\n",
    "            continue\n",
    "\n",
    "        image_t = image_t[rect[0]:rect[2], rect[1]:rect[3], :]\n",
    "        centers = (boxes[:, :2] + boxes[:, 2:4]) / 2.0\n",
    "        m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])\n",
    "        m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])\n",
    "\n",
    "        # mask in that both m1 and m2 are true\n",
    "        mask = m1 * m2 * drop_mask\n",
    "\n",
    "        # have any valid boxes? try again if not\n",
    "        if not mask.any():\n",
    "            continue\n",
    "\n",
    "        # take only matching gt boxes\n",
    "        boxes_t = boxes[mask, :].copy()\n",
    "        boxes_t[:, :2] = np.maximum(boxes_t[:, :2], rect[:2])\n",
    "        boxes_t[:, :2] -= rect[:2]\n",
    "        boxes_t[:, 2:4] = np.minimum(boxes_t[:, 2:4], rect[2:4])\n",
    "        boxes_t[:, 2:4] -= rect[:2]\n",
    "\n",
    "        return image_t, boxes_t\n",
    "    return image, boxes\n",
    "\n",
    "def ssd_bboxes_encode(boxes):\n",
    "    \"\"\"Labels anchors with ground truth inputs.\"\"\"\n",
    "\n",
    "    def jaccard_with_anchors(bbox):\n",
    "        \"\"\"Compute jaccard score a box and the anchors.\"\"\"\n",
    "        # Intersection bbox and volume.\n",
    "        ymin = np.maximum(y1, bbox[0])\n",
    "        xmin = np.maximum(x1, bbox[1])\n",
    "        ymax = np.minimum(y2, bbox[2])\n",
    "        xmax = np.minimum(x2, bbox[3])\n",
    "        w = np.maximum(xmax - xmin, 0.)\n",
    "        h = np.maximum(ymax - ymin, 0.)\n",
    "\n",
    "        # Volumes.\n",
    "        inter_vol = h * w\n",
    "        union_vol = vol_anchors + (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) - inter_vol\n",
    "        jaccard = inter_vol / union_vol\n",
    "        return np.squeeze(jaccard)\n",
    "\n",
    "    pre_scores = np.zeros((8732), dtype=np.float32)\n",
    "    t_boxes = np.zeros((8732, 4), dtype=np.float32)\n",
    "    t_label = np.zeros((8732), dtype=np.int64)\n",
    "    for bbox in boxes:\n",
    "        label = int(bbox[4])\n",
    "        scores = jaccard_with_anchors(bbox)\n",
    "        idx = np.argmax(scores)\n",
    "        scores[idx] = 2.0\n",
    "        mask = (scores > matching_threshold)\n",
    "        mask = mask & (scores > pre_scores)\n",
    "        pre_scores = np.maximum(pre_scores, scores * mask)\n",
    "        t_label = mask * label + (1 - mask) * t_label\n",
    "        for i in range(4):\n",
    "            t_boxes[:, i] = mask * bbox[i] + (1 - mask) * t_boxes[:, i]\n",
    "\n",
    "    index = np.nonzero(t_label)\n",
    "\n",
    "    # Transform to tlbr.\n",
    "    bboxes = np.zeros((8732, 4), dtype=np.float32)\n",
    "    bboxes[:, [0, 1]] = (t_boxes[:, [0, 1]] + t_boxes[:, [2, 3]]) / 2\n",
    "    bboxes[:, [2, 3]] = t_boxes[:, [2, 3]] - t_boxes[:, [0, 1]]\n",
    "\n",
    "    # Encode features.\n",
    "    bboxes_t = bboxes[index]\n",
    "    default_boxes_t = default_boxes[index]\n",
    "    bboxes_t[:, :2] = (bboxes_t[:, :2] - default_boxes_t[:, :2]) / (default_boxes_t[:, 2:] * 0.1)\n",
    "    tmp = np.maximum(bboxes_t[:, 2:4] / default_boxes_t[:, 2:4], 0.000001)\n",
    "    bboxes_t[:, 2:4] = np.log(tmp) / 0.2\n",
    "    bboxes[index] = bboxes_t\n",
    "\n",
    "    num_match = np.array([len(np.nonzero(t_label)[0])], dtype=np.int32)\n",
    "    return bboxes, t_label.astype(np.int32), num_match\n",
    "\n",
    "def preprocess_fn(img_id, image, box, is_training):\n",
    "    \"\"\"Preprocess function for dataset.\"\"\"\n",
    "    cv2.setNumThreads(2)\n",
    "\n",
    "    def _infer_data(image, input_shape):\n",
    "        img_h, img_w, _ = image.shape\n",
    "        input_h, input_w = input_shape\n",
    "\n",
    "        image = cv2.resize(image, (input_w, input_h))\n",
    "\n",
    "        # When the channels of image is 1\n",
    "        if len(image.shape) == 2:\n",
    "            image = np.expand_dims(image, axis=-1)\n",
    "            image = np.concatenate([image, image, image], axis=-1)\n",
    "\n",
    "        return img_id, image, np.array((img_h, img_w), np.float32)\n",
    "\n",
    "    def _data_aug(image, box, is_training, image_size=(300, 300)):\n",
    "        ih, iw, _ = image.shape\n",
    "        h, w = image_size\n",
    "        if not is_training:\n",
    "            return _infer_data(image, image_size)\n",
    "        # Random crop\n",
    "        box = box.astype(np.float32)\n",
    "        image, box = random_sample_crop(image, box)\n",
    "        ih, iw, _ = image.shape\n",
    "        # Resize image\n",
    "        image = cv2.resize(image, (w, h))\n",
    "        # Flip image or not\n",
    "        flip = _rand() < .5\n",
    "        if flip:\n",
    "            image = cv2.flip(image, 1, dst=None)\n",
    "        # When the channels of image is 1\n",
    "        if len(image.shape) == 2:\n",
    "            image = np.expand_dims(image, axis=-1)\n",
    "            image = np.concatenate([image, image, image], axis=-1)\n",
    "        box[:, [0, 2]] = box[:, [0, 2]] / ih\n",
    "        box[:, [1, 3]] = box[:, [1, 3]] / iw\n",
    "        if flip:\n",
    "            box[:, [1, 3]] = 1 - box[:, [3, 1]]\n",
    "        box, label, num_match = ssd_bboxes_encode(box)\n",
    "        return image, box, label, num_match\n",
    "\n",
    "    return _data_aug(image, box, is_training, image_size=[300, 300])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac590832",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 数据集创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46071849",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from mindspore import Tensor\n",
    "from mindspore.dataset import MindDataset\n",
    "from mindspore.dataset.vision import Decode, HWC2CHW, Normalize, RandomColorAdjust\n",
    "\n",
    "\n",
    "def create_ssd_dataset(mindrecord_file, batch_size=32, device_num=1, rank=0,\n",
    "                       is_training=True, num_parallel_workers=1, use_multiprocessing=True):\n",
    "    \"\"\"Create SSD dataset with MindDataset.\"\"\"\n",
    "    dataset = MindDataset(mindrecord_file, columns_list=[\"img_id\", \"image\", \"annotation\"], num_shards=device_num,\n",
    "                          shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)\n",
    "\n",
    "    decode = Decode()\n",
    "    dataset = dataset.map(operations=decode, input_columns=[\"image\"])\n",
    "\n",
    "    change_swap_op = HWC2CHW()\n",
    "    # Computed from random subset of ImageNet training images\n",
    "    normalize_op = Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n",
    "                             std=[0.229 * 255, 0.224 * 255, 0.225 * 255])\n",
    "    color_adjust_op = RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)\n",
    "    compose_map_func = (lambda img_id, image, annotation: preprocess_fn(img_id, image, annotation, is_training))\n",
    "\n",
    "    if is_training:\n",
    "        output_columns = [\"image\", \"box\", \"label\", \"num_match\"]\n",
    "        trans = [color_adjust_op, normalize_op, change_swap_op]\n",
    "    else:\n",
    "        output_columns = [\"img_id\", \"image\", \"image_shape\"]\n",
    "        trans = [normalize_op, change_swap_op]\n",
    "\n",
    "    dataset = dataset.map(operations=compose_map_func, input_columns=[\"img_id\", \"image\", \"annotation\"],\n",
    "                          output_columns=output_columns, python_multiprocessing=use_multiprocessing,\n",
    "                          num_parallel_workers=num_parallel_workers)\n",
    "\n",
    "    dataset = dataset.map(operations=trans, input_columns=[\"image\"], python_multiprocessing=use_multiprocessing,\n",
    "                          num_parallel_workers=num_parallel_workers)\n",
    "\n",
    "    dataset = dataset.batch(batch_size, drop_remainder=True)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb61c0c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 模型构建\n",
    "\n",
    "SSD的网络结构主要分为以下几个部分：\n",
    "\n",
    "![SSD-3](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_3.jpg)\n",
    "\n",
    "- VGG16 Base Layer\n",
    "\n",
    "- Extra Feature Layer\n",
    "\n",
    "- Detection Layer\n",
    "\n",
    "- NMS\n",
    "\n",
    "- Anchor\n",
    "\n",
    "### Backbone Layer\n",
    "\n",
    "![SSD-4](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_4.png)\n",
    "\n",
    "输入图像经过预处理后大小固定为300×300，首先经过backbone，本案例中使用的是VGG16网络的前13个卷积层，然后分别将VGG16的全连接层fc6和fc7转换成3 $\\times$ 3卷积层block6和1 $\\times$ 1卷积层block7，进一步提取特征。 在block6中，使用了空洞数为6的空洞卷积，其padding也为6，这样做同样也是为了增加感受野的同时保持参数量与特征图尺寸的不变。\n",
    "\n",
    "### Extra Feature Layer\n",
    "\n",
    "在VGG16的基础上，SSD进一步增加了4个深度卷积层，用于提取更高层的语义信息：\n",
    "\n",
    "![SSD-5](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_5.png)\n",
    "\n",
    "block8-11，用于更高语义信息的提取。block8的通道数为512，而block9、block10与block11的通道数都为256。从block7到block11，这5个卷积后输出特征图的尺寸依次为19×19、10×10、5×5、3×3和1×1。为了降低参数量，使用了1×1卷积先降低通道数为该层输出通道数的一半，再利用3×3卷积进行特征提取。\n",
    "\n",
    "### Anchor\n",
    "\n",
    "SSD采用了PriorBox来进行区域生成。将固定大小宽高的PriorBox作为先验的感兴趣区域，利用一个阶段完成能够分类与回归。设计大量的密集的PriorBox保证了对整幅图像的每个地方都有检测。PriorBox位置的表示形式是以中心点坐标和框的宽、高(cx,cy,w,h)来表示的，同时都转换成百分比的形式。\n",
    "PriorBox生成规则：\n",
    "SSD由6个特征层来检测目标，在不同特征层上，PriorBox的尺寸scale大小是不一样的，最低层的scale=0.1，最高层的scale=0.95，其他层的计算公式如下：\n",
    "\n",
    "![SSD-6](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_6.jpg)\n",
    "\n",
    "在某个特征层上其scale一定，那么会设置不同长宽比ratio的PriorBox，其长和宽的计算公式如下：\n",
    "\n",
    "![SSD-7](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_7.jpg)\n",
    "\n",
    "在ratio=1的时候，还会根据该特征层和下一个特征层计算一个特定scale的PriorBox(长宽比ratio=1)，计算公式如下：\n",
    "\n",
    "![SSD-8](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_8.jpg)\n",
    "\n",
    "每个特征层的每个点都会以上述规则生成PriorBox，(cx,cy)由当前点的中心点来确定，由此每个特征层都生成大量密集的PriorBox，如下图：\n",
    "\n",
    "![SSD-9](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_9.png)\n",
    "\n",
    "SSD使用了第4、7、8、9、10和11这6个卷积层得到的特征图，这6个特征图尺寸越来越小，而其对应的感受野越来越大。6个特征图上的每一个点分别对应4、6、6、6、4、4个PriorBox。某个特征图上的一个点根据下采样率可以得到在原图的坐标，以该坐标为中心生成4个或6个不同大小的PriorBox，然后利用特征图的特征去预测每一个PriorBox对应类别与位置的预测量。例如：第8个卷积层得到的特征图大小为10×10×512，每个点对应6个PriorBox，一共有600个PriorBox。定义MultiBox类，生成多个预测框。\n",
    "\n",
    "### Detection Layer\n",
    "\n",
    "![SSD-10](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_10.jpg)\n",
    "\n",
    "SSD模型一共有6个预测特征图，对于其中一个尺寸为m\\*n，通道为p的预测特征图，假设其每个像素点会产生k个anchor，每个anchor会对应c个类别和4个回归偏移量，使用(4+c)k个尺寸为3x3，通道为p的卷积核对该预测特征图进行卷积操作，得到尺寸为m\\*n，通道为(4+c)m\\*k的输出特征图，它包含了预测特征图上所产生的每个anchor的回归偏移量和各类别概率分数。所以对于尺寸为m\\*n的预测特征图，总共会产生(4+c)k\\*m\\*n个结果。cls分支的输出通道数为k\\*class_num，loc分支的输出通道数为k\\*4。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ba72ae2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import mxnet as mx\n",
    "from mxnet import autograd, gluon, image, init, np, npx\n",
    "from d2l import mxnet as d2l\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "device = mx.cpu()  # 若使用 GPU，可改为 mx.gpu()\n",
    "\n",
    "# 类别预测层\n",
    "def cls_predictor(num_anchors, num_classes):\n",
    "    return gluon.nn.Conv2D(num_anchors * (num_classes + 1), kernel_size=3,\n",
    "                           padding=1)\n",
    "\n",
    "# 边界框预测层\n",
    "def bbox_predictor(num_anchors):\n",
    "    return gluon.nn.Conv2D(num_anchors * 4, kernel_size=3, padding=1)\n",
    "\n",
    "# 展平预测结果\n",
    "def flatten_pred(pred):\n",
    "    return npx.batch_flatten(pred.transpose(0, 2, 3, 1))\n",
    "\n",
    "# 连接多个预测结果\n",
    "def concat_preds(preds):\n",
    "    return np.concatenate([flatten_pred(p) for p in preds], axis=1)\n",
    "\n",
    "# 高和宽减半块\n",
    "def down_sample_blk(num_channels):\n",
    "    blk = gluon.nn.Sequential()\n",
    "    for _ in range(2):\n",
    "        blk.add(gluon.nn.Conv2D(num_channels, kernel_size=3, padding=1),\n",
    "                gluon.nn.BatchNorm(in_channels=num_channels),\n",
    "                gluon.nn.Activation('relu'))\n",
    "    blk.add(gluon.nn.MaxPool2D(2))\n",
    "    return blk\n",
    "\n",
    "# 基本网络块\n",
    "def base_net():\n",
    "    blk = gluon.nn.Sequential()\n",
    "    for num_filters in [16, 32, 64]:\n",
    "        blk.add(down_sample_blk(num_filters))\n",
    "    return blk\n",
    "\n",
    "# 获取块\n",
    "def get_blk(i):\n",
    "    if i == 0:\n",
    "        blk = base_net()\n",
    "    elif i == 4:\n",
    "        blk = gluon.nn.GlobalMaxPool2D()\n",
    "    else:\n",
    "        blk = down_sample_blk(128)\n",
    "    return blk\n",
    "\n",
    "# 块的前向传播\n",
    "def blk_forward(X, blk, size, ratio, cls_predictor, bbox_predictor):\n",
    "    Y = blk(X)\n",
    "    anchors = d2l.multibox_prior(Y, sizes=size, ratios=ratio)\n",
    "    cls_preds = cls_predictor(Y)\n",
    "    bbox_preds = bbox_predictor(Y)\n",
    "    return (Y, anchors, cls_preds, bbox_preds)\n",
    "\n",
    "# TinySSD模型\n",
    "class TinySSD(gluon.nn.Block):\n",
    "    def __init__(self, num_classes, **kwargs):\n",
    "        super(TinySSD, self).__init__(**kwargs)\n",
    "        self.num_classes = num_classes\n",
    "        for i in range(5):\n",
    "            # 即赋值语句self.blk_i=get_blk(i)\n",
    "            setattr(self, f'blk_{i}', get_blk(i))\n",
    "            setattr(self, f'cls_{i}', cls_predictor(num_anchors, num_classes))\n",
    "            setattr(self, f'bbox_{i}', bbox_predictor(num_anchors))\n",
    "\n",
    "    def forward(self, X):\n",
    "        anchors, cls_preds, bbox_preds = [None] * 5, [None] * 5, [None] * 5\n",
    "        for i in range(5):\n",
    "            # getattr(self,'blk_%d'%i)即访问self.blk_i\n",
    "            X, anchors[i], cls_preds[i], bbox_preds[i] = blk_forward(\n",
    "                X, getattr(self, f'blk_{i}'), sizes[i], ratios[i],\n",
    "                getattr(self, f'cls_{i}'), getattr(self, f'bbox_{i}'))\n",
    "        anchors = np.concatenate(anchors, axis=1)\n",
    "        cls_preds = concat_preds(cls_preds)\n",
    "        cls_preds = cls_preds.reshape(\n",
    "            cls_preds.shape[0], -1, self.num_classes + 1)\n",
    "        bbox_preds = concat_preds(bbox_preds)\n",
    "        return anchors, cls_preds, bbox_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1942062",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 损失函数\n",
    "\n",
    "SSD算法的目标函数分为两部分：计算相应的预选框与目标类别的置信度误差（confidence loss, conf）以及相应的位置误差（locatization loss， loc）：\n",
    "\n",
    "![SSD-11](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_11.jpg)\n",
    "\n",
    "其中：<br />\n",
    "N 是先验框的正样本数量；<br />\n",
    "c 为类别置信度预测值; <br />\n",
    "l 为先验框的所对应边界框的位置预测值; <br />\n",
    "g 为ground truth的位置参数  <br />\n",
    "α 用以调整confidence loss和location loss之间的比例，默认为1。\n",
    "\n",
    "### 对于位置损失函数\n",
    "\n",
    "针对所有的正样本，采用 Smooth L1 Loss, 位置信息都是 encode 之后的位置信息。\n",
    "\n",
    "![SSD-12](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_12.jpg)\n",
    "\n",
    "### 对于置信度损失函数\n",
    "\n",
    "置信度损失是多类置信度(c)上的softmax损失。\n",
    "\n",
    "![SSD-13](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_13.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9a35c7c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def class_loss(logits, label):\n",
    "    \"\"\"Calculate category losses.\"\"\"\n",
    "    label = ops.one_hot(label, ops.shape(logits)[-1], Tensor(1.0, ms.float32), Tensor(0.0, ms.float32))\n",
    "    weight = ops.ones_like(logits)\n",
    "    pos_weight = ops.ones_like(logits)\n",
    "    sigmiod_cross_entropy = ops.binary_cross_entropy_with_logits(logits, label, weight.astype(ms.float32), pos_weight.astype(ms.float32))\n",
    "    sigmoid = ops.sigmoid(logits)\n",
    "    label = label.astype(ms.float32)\n",
    "    p_t = label * sigmoid + (1 - label) * (1 - sigmoid)\n",
    "    modulating_factor = ops.pow(1 - p_t, 2.0)\n",
    "    alpha_weight_factor = label * 0.75 + (1 - label) * (1 - 0.75)\n",
    "    focal_loss = modulating_factor * alpha_weight_factor * sigmiod_cross_entropy\n",
    "    return focal_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10871aeb",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Metrics\n",
    "\n",
    "在SSD中，训练过程是不需要用到非极大值抑制(NMS)，但当进行检测时，例如输入一张图片要求输出框的时候，需要用到NMS过滤掉那些重叠度较大的预测框。<br />\n",
    "非极大值抑制的流程如下：\n",
    "\n",
    "1. 根据置信度得分进行排序\n",
    "\n",
    "2. 选择置信度最高的比边界框添加到最终输出列表中，将其从边界框列表中删除<br />\n",
    "\n",
    "3. 计算所有边界框的面积<br />\n",
    "\n",
    "4. 计算置信度最高的边界框与其他候选框的IoU<br />\n",
    "\n",
    "5. 删除IoU大于阈值的边界框<br />\n",
    "\n",
    "6. 重复上述过程，直至边界框列表为空<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cea96244",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_137479/1464347597.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mSsdInferWithDecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m     \"\"\"\n\u001b[1;32m    138\u001b[0m SSD Infer wrapper to decode the bbox locations.\"\"\"\n",
      "\u001b[0;31mNameError\u001b[0m: name 'nn' is not defined"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from pycocotools.coco import COCO\n",
    "from pycocotools.cocoeval import COCOeval\n",
    "\n",
    "\n",
    "def apply_eval(eval_param_dict):\n",
    "    net = eval_param_dict[\"net\"]\n",
    "    net.set_train(False)\n",
    "    ds = eval_param_dict[\"dataset\"]\n",
    "    anno_json = eval_param_dict[\"anno_json\"]\n",
    "    coco_metrics = COCOMetrics(anno_json=anno_json,\n",
    "                               classes=train_cls,\n",
    "                               num_classes=81,\n",
    "                               max_boxes=100,\n",
    "                               nms_threshold=0.6,\n",
    "                               min_score=0.1)\n",
    "    for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):\n",
    "        img_id = data['img_id']\n",
    "        img_np = data['image']\n",
    "        image_shape = data['image_shape']\n",
    "\n",
    "        output = net(Tensor(img_np))\n",
    "\n",
    "        for batch_idx in range(img_np.shape[0]):\n",
    "            pred_batch = {\n",
    "                \"boxes\": output[0].asnumpy()[batch_idx],\n",
    "                \"box_scores\": output[1].asnumpy()[batch_idx],\n",
    "                \"img_id\": int(np.squeeze(img_id[batch_idx])),\n",
    "                \"image_shape\": image_shape[batch_idx]\n",
    "            }\n",
    "            coco_metrics.update(pred_batch)\n",
    "    eval_metrics = coco_metrics.get_metrics()\n",
    "    return eval_metrics\n",
    "\n",
    "\n",
    "def apply_nms(all_boxes, all_scores, thres, max_boxes):\n",
    "    \"\"\"Apply NMS to bboxes.\"\"\"\n",
    "    y1 = all_boxes[:, 0]\n",
    "    x1 = all_boxes[:, 1]\n",
    "    y2 = all_boxes[:, 2]\n",
    "    x2 = all_boxes[:, 3]\n",
    "    areas = (x2 - x1 + 1) * (y2 - y1 + 1)\n",
    "\n",
    "    order = all_scores.argsort()[::-1]\n",
    "    keep = []\n",
    "\n",
    "    while order.size > 0:\n",
    "        i = order[0]\n",
    "        keep.append(i)\n",
    "\n",
    "        if len(keep) >= max_boxes:\n",
    "            break\n",
    "\n",
    "        xx1 = np.maximum(x1[i], x1[order[1:]])\n",
    "        yy1 = np.maximum(y1[i], y1[order[1:]])\n",
    "        xx2 = np.minimum(x2[i], x2[order[1:]])\n",
    "        yy2 = np.minimum(y2[i], y2[order[1:]])\n",
    "\n",
    "        w = np.maximum(0.0, xx2 - xx1 + 1)\n",
    "        h = np.maximum(0.0, yy2 - yy1 + 1)\n",
    "        inter = w * h\n",
    "\n",
    "        ovr = inter / (areas[i] + areas[order[1:]] - inter)\n",
    "\n",
    "        inds = np.where(ovr <= thres)[0]\n",
    "\n",
    "        order = order[inds + 1]\n",
    "    return keep\n",
    "\n",
    "\n",
    "class COCOMetrics:\n",
    "    \"\"\"Calculate mAP of predicted bboxes.\"\"\"\n",
    "\n",
    "    def __init__(self, anno_json, classes, num_classes, min_score, nms_threshold, max_boxes):\n",
    "        self.num_classes = num_classes\n",
    "        self.classes = classes\n",
    "        self.min_score = min_score\n",
    "        self.nms_threshold = nms_threshold\n",
    "        self.max_boxes = max_boxes\n",
    "\n",
    "        self.val_cls_dict = {i: cls for i, cls in enumerate(classes)}\n",
    "        self.coco_gt = COCO(anno_json)\n",
    "        cat_ids = self.coco_gt.loadCats(self.coco_gt.getCatIds())\n",
    "        self.class_dict = {cat['name']: cat['id'] for cat in cat_ids}\n",
    "\n",
    "        self.predictions = []\n",
    "        self.img_ids = []\n",
    "\n",
    "    def update(self, batch):\n",
    "        pred_boxes = batch['boxes']\n",
    "        box_scores = batch['box_scores']\n",
    "        img_id = batch['img_id']\n",
    "        h, w = batch['image_shape']\n",
    "\n",
    "        final_boxes = []\n",
    "        final_label = []\n",
    "        final_score = []\n",
    "        self.img_ids.append(img_id)\n",
    "\n",
    "        for c in range(1, self.num_classes):\n",
    "            class_box_scores = box_scores[:, c]\n",
    "            score_mask = class_box_scores > self.min_score\n",
    "            class_box_scores = class_box_scores[score_mask]\n",
    "            class_boxes = pred_boxes[score_mask] * [h, w, h, w]\n",
    "\n",
    "            if score_mask.any():\n",
    "                nms_index = apply_nms(class_boxes, class_box_scores, self.nms_threshold, self.max_boxes)\n",
    "                class_boxes = class_boxes[nms_index]\n",
    "                class_box_scores = class_box_scores[nms_index]\n",
    "\n",
    "                final_boxes += class_boxes.tolist()\n",
    "                final_score += class_box_scores.tolist()\n",
    "                final_label += [self.class_dict[self.val_cls_dict[c]]] * len(class_box_scores)\n",
    "\n",
    "        for loc, label, score in zip(final_boxes, final_label, final_score):\n",
    "            res = {}\n",
    "            res['image_id'] = img_id\n",
    "            res['bbox'] = [loc[1], loc[0], loc[3] - loc[1], loc[2] - loc[0]]\n",
    "            res['score'] = score\n",
    "            res['category_id'] = label\n",
    "            self.predictions.append(res)\n",
    "\n",
    "    def get_metrics(self):\n",
    "        with open('predictions.json', 'w') as f:\n",
    "            json.dump(self.predictions, f)\n",
    "\n",
    "        coco_dt = self.coco_gt.loadRes('predictions.json')\n",
    "        E = COCOeval(self.coco_gt, coco_dt, iouType='bbox')\n",
    "        E.params.imgIds = self.img_ids\n",
    "        E.evaluate()\n",
    "        E.accumulate()\n",
    "        E.summarize()\n",
    "        return E.stats[0]\n",
    "\n",
    "\n",
    "class SsdInferWithDecoder(nn.Cell):\n",
    "    \"\"\"\n",
    "SSD Infer wrapper to decode the bbox locations.\"\"\"\n",
    "\n",
    "    def __init__(self, network, default_boxes, ckpt_path):\n",
    "        super(SsdInferWithDecoder, self).__init__()\n",
    "        param_dict = ms.load_checkpoint(ckpt_path)\n",
    "        ms.load_param_into_net(network, param_dict)\n",
    "        self.network = network\n",
    "        self.default_boxes = default_boxes\n",
    "        self.prior_scaling_xy = 0.1\n",
    "        self.prior_scaling_wh = 0.2\n",
    "\n",
    "    def construct(self, x):\n",
    "        pred_loc, pred_label = self.network(x)\n",
    "\n",
    "        default_bbox_xy = self.default_boxes[..., :2]\n",
    "        default_bbox_wh = self.default_boxes[..., 2:]\n",
    "        pred_xy = pred_loc[..., :2] * self.prior_scaling_xy * default_bbox_wh + default_bbox_xy\n",
    "        pred_wh = ops.exp(pred_loc[..., 2:] * self.prior_scaling_wh) * default_bbox_wh\n",
    "\n",
    "        pred_xy_0 = pred_xy - pred_wh / 2.0\n",
    "        pred_xy_1 = pred_xy + pred_wh / 2.0\n",
    "        pred_xy = ops.concat((pred_xy_0, pred_xy_1), -1)\n",
    "        pred_xy = ops.maximum(pred_xy, 0)\n",
    "        pred_xy = ops.minimum(pred_xy, 1)\n",
    "        return pred_xy, pred_label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce744ef",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 训练过程\n",
    "\n",
    "### （1）先验框匹配\n",
    "\n",
    "在训练过程中，首先要确定训练图片中的ground truth（真实目标）与哪个先验框来进行匹配，与之匹配的先验框所对应的边界框将负责预测它。\n",
    "\n",
    "SSD的先验框与ground truth的匹配原则主要有两点：\n",
    "\n",
    "1. 对于图片中每个ground truth，找到与其IOU最大的先验框，该先验框与其匹配，这样可以保证每个ground truth一定与某个先验框匹配。通常称与ground truth匹配的先验框为正样本，反之，若一个先验框没有与任何ground truth进行匹配，那么该先验框只能与背景匹配，就是负样本。\n",
    "\n",
    "2. 对于剩余的未匹配先验框，若某个ground truth的IOU大于某个阈值（一般是0.5），那么该先验框也与这个ground truth进行匹配。尽管一个ground truth可以与多个先验框匹配，但是ground truth相对先验框还是太少了，所以负样本相对正样本会很多。为了保证正负样本尽量平衡，SSD采用了hard negative mining，就是对负样本进行抽样，抽样时按照置信度误差（预测背景的置信度越小，误差越大）进行降序排列，选取误差的较大的top-k作为训练的负样本，以保证正负样本比例接近1:3。\n",
    "\n",
    "注意点：\n",
    "\n",
    "1. 通常称与gt匹配的prior为正样本，反之，若某一个prior没有与任何一个gt匹配，则为负样本。\n",
    "\n",
    "2. 某个gt可以和多个prior匹配，而每个prior只能和一个gt进行匹配。\n",
    "\n",
    "3. 如果多个gt和某一个prior的IOU均大于阈值，那么prior只与IOU最大的那个进行匹配。\n",
    "\n",
    "![SSD-14](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_14.jpg)\n",
    "\n",
    "如上图所示，训练过程中的 prior boxes 和 ground truth boxes 的匹配，基本思路是：让每一个 prior box 回归并且到 ground truth box，这个过程的调控我们需要损失层的帮助，他会计算真实值和预测值之间的误差，从而指导学习的走向。\n",
    "\n",
    "### （2）损失函数\n",
    "\n",
    "损失函数使用的是上文提到的位置损失函数和置信度损失函数的加权和。\n",
    "\n",
    "### （3）数据增强\n",
    "\n",
    "使用之前定义好的数据增强方式，对创建好的数据增强方式进行数据增强。\n",
    "\n",
    "模型训练时，设置模型训练的epoch次数为60，然后通过create_ssd_dataset类创建了训练集和验证集。batch_size大小为5，图像尺寸统一调整为300×300。损失函数使用位置损失函数和置信度损失函数的加权和，优化器使用Momentum，并设置初始学习率为0.001。回调函数方面使用了LossMonitor和TimeMonitor来监控训练过程中每个epoch结束后，损失值Loss的变化情况以及每个epoch、每个step的运行时间。设置每训练10个epoch保存一次模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b4bbd84",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import itertools as it\n",
    "\n",
    "from mindspore import set_seed\n",
    "\n",
    "class GeneratDefaultBoxes():\n",
    "    \"\"\"\n",
    "    Generate Default boxes for SSD, follows the order of (W, H, archor_sizes).\n",
    "    `self.default_boxes` has a shape of [archor_sizes, H, W, 4], the last dimension is [y, x, h, w].\n",
    "    `self.default_boxes_tlbr` has a shape as `self.default_boxes`, the last dimension is [y1, x1, y2, x2].\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        fk = 300 / np.array([8, 16, 32, 64, 100, 300])\n",
    "        scale_rate = (0.95 - 0.1) / (len([4, 6, 6, 6, 4, 4]) - 1)\n",
    "        scales = [0.1 + scale_rate * i for i in range(len([4, 6, 6, 6, 4, 4]))] + [1.0]\n",
    "        self.default_boxes = []\n",
    "        for idex, feature_size in enumerate([38, 19, 10, 5, 3, 1]):\n",
    "            sk1 = scales[idex]\n",
    "            sk2 = scales[idex + 1]\n",
    "            sk3 = math.sqrt(sk1 * sk2)\n",
    "            if idex == 0 and not [[2], [2, 3], [2, 3], [2, 3], [2], [2]][idex]:\n",
    "                w, h = sk1 * math.sqrt(2), sk1 / math.sqrt(2)\n",
    "                all_sizes = [(0.1, 0.1), (w, h), (h, w)]\n",
    "            else:\n",
    "                all_sizes = [(sk1, sk1)]\n",
    "                for aspect_ratio in [[2], [2, 3], [2, 3], [2, 3], [2], [2]][idex]:\n",
    "                    w, h = sk1 * math.sqrt(aspect_ratio), sk1 / math.sqrt(aspect_ratio)\n",
    "                    all_sizes.append((w, h))\n",
    "                    all_sizes.append((h, w))\n",
    "                all_sizes.append((sk3, sk3))\n",
    "\n",
    "            assert len(all_sizes) == [4, 6, 6, 6, 4, 4][idex]\n",
    "\n",
    "            for i, j in it.product(range(feature_size), repeat=2):\n",
    "                for w, h in all_sizes:\n",
    "                    cx, cy = (j + 0.5) / fk[idex], (i + 0.5) / fk[idex]\n",
    "                    self.default_boxes.append([cy, cx, h, w])\n",
    "\n",
    "        def to_tlbr(cy, cx, h, w):\n",
    "            return cy - h / 2, cx - w / 2, cy + h / 2, cx + w / 2\n",
    "\n",
    "        # For IoU calculation\n",
    "        self.default_boxes_tlbr = np.array(tuple(to_tlbr(*i) for i in self.default_boxes), dtype='float32')\n",
    "        self.default_boxes = np.array(self.default_boxes, dtype='float32')\n",
    "\n",
    "default_boxes_tlbr = GeneratDefaultBoxes().default_boxes_tlbr\n",
    "default_boxes = GeneratDefaultBoxes().default_boxes\n",
    "\n",
    "y1, x1, y2, x2 = np.split(default_boxes_tlbr[:, :4], 4, axis=-1)\n",
    "vol_anchors = (x2 - x1) * (y2 - y1)\n",
    "matching_threshold = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "faae8a3f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from mindspore.common.initializer import initializer, TruncatedNormal\n",
    "\n",
    "\n",
    "def init_net_param(network, initialize_mode='TruncatedNormal'):\n",
    "    \"\"\"Init the parameters in net.\"\"\"\n",
    "    params = network.trainable_params()\n",
    "    for p in params:\n",
    "        if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:\n",
    "            if initialize_mode == 'TruncatedNormal':\n",
    "                p.set_data(initializer(TruncatedNormal(0.02), p.data.shape, p.data.dtype))\n",
    "            else:\n",
    "                p.set_data(initialize_mode, p.data.shape, p.data.dtype)\n",
    "\n",
    "\n",
    "def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):\n",
    "    \"\"\" generate learning rate array\"\"\"\n",
    "    lr_each_step = []\n",
    "    total_steps = steps_per_epoch * total_epochs\n",
    "    warmup_steps = steps_per_epoch * warmup_epochs\n",
    "    for i in range(total_steps):\n",
    "        if i < warmup_steps:\n",
    "            lr = lr_init + (lr_max - lr_init) * i / warmup_steps\n",
    "        else:\n",
    "            lr = lr_end + (lr_max - lr_end) * (1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.\n",
    "        if lr < 0.0:\n",
    "            lr = 0.0\n",
    "        lr_each_step.append(lr)\n",
    "\n",
    "    current_step = global_step\n",
    "    lr_each_step = np.array(lr_each_step).astype(np.float32)\n",
    "    learning_rate = lr_each_step[current_step:]\n",
    "\n",
    "    return learning_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c739ddf",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sizes = [[0.2, 0.272], [0.37, 0.447], [0.54, 0.619], [0.71, 0.79],\n",
    "         [0.88, 0.961]]\n",
    "ratios = [[1, 2, 0.5]] * 5\n",
    "num_anchors = len(sizes[0]) + len(ratios[0]) - 1\n",
    "\n",
    "net = TinySSD(1)\n",
    "batch_size = 32\n",
    "train_iter, _ = d2l.load_data_bananas(batch_size)\n",
    "\n",
    "cls_loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
    "bbox_loss = gluon.loss.L1Loss()\n",
    "\n",
    "def calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks):\n",
    "    cls = cls_loss(cls_preds, cls_labels)\n",
    "    bbox = bbox_loss(bbox_preds * bbox_masks, bbox_labels * bbox_masks)\n",
    "    return cls + bbox\n",
    "\n",
    "def cls_eval(cls_preds, cls_labels):\n",
    "    # 由于类别预测结果放在最后一维，argmax需要指定最后一维。\n",
    "    return float((cls_preds.argmax(axis=-1).astype(\n",
    "        cls_labels.dtype) == cls_labels).sum())\n",
    "\n",
    "def bbox_eval(bbox_preds, bbox_labels, bbox_masks):\n",
    "    return float((np.abs((bbox_labels - bbox_preds) * bbox_masks)).sum())\n",
    "\n",
    "net.initialize(init=init.Xavier(), ctx=device)\n",
    "trainer = gluon.Trainer(net.collect_params(),'sgd',\n",
    "                        {'learning_rate': 0.2, 'wd': 5e-4})\n",
    "\n",
    "num_epochs, timer = 20, d2l.Timer()\n",
    "animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\n",
    "                        legend=['class error', 'bbox mae'])\n",
    "\n",
    "total_losses = []\n",
    "cls_losses = []\n",
    "pos_losses = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # 指标包括：训练精确度的和，训练精确度的和中的示例数，\n",
    "    # 绝对误差的和，绝对误差的和中的示例数\n",
    "    metric = d2l.Accumulator(4)\n",
    "    epoch_total_loss = 0\n",
    "    epoch_cls_loss = 0\n",
    "    epoch_pos_loss = 0\n",
    "    for features, target in train_iter:\n",
    "        timer.start()\n",
    "        X = features.as_in_ctx(device)\n",
    "        Y = target.as_in_ctx(device)\n",
    "        with autograd.record():\n",
    "            # 生成多尺度的锚框，为每个锚框预测类别和偏移量\n",
    "            anchors, cls_preds, bbox_preds = net(X)\n",
    "            # 为每个锚框标注类别和偏移量\n",
    "            bbox_labels, bbox_masks, cls_labels = d2l.multibox_target(anchors,\n",
    "                                                                      Y)\n",
    "            # 计算分类损失\n",
    "            cls = cls_loss(cls_preds, cls_labels)\n",
    "            # 计算正样本框回归损失\n",
    "            bbox = bbox_loss(bbox_preds * bbox_masks, bbox_labels * bbox_masks)\n",
    "            # 计算总损失\n",
    "            l = cls + bbox\n",
    "            epoch_total_loss += l.sum().item()\n",
    "            epoch_cls_loss += cls.sum().item()\n",
    "            epoch_pos_loss += bbox.sum().item()\n",
    "        l.backward()\n",
    "        trainer.step(batch_size)\n",
    "        metric.add(cls_eval(cls_preds, cls_labels), cls_labels.size,\n",
    "                   bbox_eval(bbox_preds, bbox_labels, bbox_masks),\n",
    "                   bbox_labels.size)\n",
    "    cls_err, bbox_mae = 1 - metric[0] / metric[1], metric[2] / metric[3]\n",
    "    animator.add(epoch + 1, (cls_err, bbox_mae))\n",
    "    total_losses.append(epoch_total_loss)\n",
    "    cls_losses.append(epoch_cls_loss)\n",
    "    pos_losses.append(epoch_pos_loss)\n",
    "\n",
    "    print(f'Epoch {epoch + 1}: Total Loss = {epoch_total_loss:.4f}, '\n",
    "          f'Classification Loss = {epoch_cls_loss:.4f}, '\n",
    "          f'Positive Bbox Loss = {epoch_pos_loss:.4f}, '\n",
    "          f'Class Error = {cls_err:.2e}, Bbox MAE = {bbox_mae:.2e}')\n",
    "\n",
    "print(f'Final class err {cls_err:.2e}, bbox mae {bbox_mae:.2e}')\n",
    "print(f'{len(train_iter._dataset) / timer.stop():.1f} examples/sec on '\n",
    "      f'{str(device)}')\n",
    "\n",
    "# 绘制损失曲线\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(total_losses, label='Total Loss')\n",
    "plt.plot(cls_losses, label='Classification Loss')\n",
    "plt.plot(pos_losses, label='Positive Bbox Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Losses during Training')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 保存模型\n",
    "final_total_loss = total_losses[-1]\n",
    "model_name = f'ssd20_{final_total_loss:.4f}.ckpt'\n",
    "net.save_parameters(model_name)\n",
    "print(f\"Model saved as {model_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a978b0f",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 评估\n",
    "\n",
    "自定义eval_net()类对训练好的模型进行评估，调用了上述定义的SsdInferWithDecoder类返回预测的坐标及标签，然后分别计算了在不同的IoU阈值、area和maxDets设置下的Average Precision（AP）和Average Recall（AR）。使用COCOMetrics类计算mAP。模型在测试集上的评估指标如下。\n",
    "\n",
    "### 精确率（AP）和召回率（AR）的解释\n",
    "\n",
    "- TP：IoU>设定的阈值的检测框数量（同一Ground Truth只计算一次）。\n",
    "\n",
    "- FP：IoU<=设定的阈值的检测框，或者是检测到同一个GT的多余检测框的数量。\n",
    "\n",
    "- FN：没有检测到的GT的数量。\n",
    "\n",
    "### 精确率（AP）和召回率（AR）的公式\n",
    "\n",
    "- 精确率（Average Precision,AP）：\n",
    "\n",
    "    ![SSD-15](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_15.jpg)\n",
    "\n",
    "    精确率是将正样本预测正确的结果与正样本预测的结果和预测错误的结果的和的比值，主要反映出预测结果错误率。\n",
    "\n",
    "- 召回率（Average Recall,AR）：\n",
    "\n",
    "    ![SSD-16](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.4.0/tutorials/source_zh_cn/cv/images/SSD_16.jpg)\n",
    "\n",
    "    召回率是正样本预测正确的结果与正样本预测正确的结果和正样本预测错误的和的比值，主要反映出来的是预测结果中的漏检率。\n",
    "\n",
    "### 关于以下代码运行结果的输出指标\n",
    "\n",
    "- 第一个值即为mAP(mean Average Precision), 即各类别AP的平均值。\n",
    "\n",
    "- 第二个值是iou取0.5的mAP值，是voc的评判标准。\n",
    "\n",
    "- 第三个值是评判较为严格的mAP值，可以反应算法框的位置精准程度；中间几个数为物体大小的mAP值。\n",
    "\n",
    "对于AR看一下maxDets=10/100的mAR值，反应检出率，如果两者接近，说明对于这个数据集来说，不用检测出100个框，可以提高性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "61007796",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
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   "source": [
    "img = image.imread('image.jpg')\n",
    "feature = image.imresize(img, 256, 256).astype('float32')\n",
    "X = np.expand_dims(feature.transpose(2, 0, 1), axis=0)\n",
    "\n",
    "def predict(X):\n",
    "    anchors, cls_preds, bbox_preds = net(X.as_in_ctx(device))\n",
    "    cls_probs = npx.softmax(cls_preds).transpose(0, 2, 1)\n",
    "    output = d2l.multibox_detection(cls_probs, bbox_preds, anchors)\n",
    "    idx = [i for i, row in enumerate(output[0]) if row[0] != -1]\n",
    "    return output[0, idx]\n",
    "\n",
    "output = predict(X)\n",
    "\n",
    "def display(img, output, threshold):\n",
    "    d2l.set_figsize((5, 5))\n",
    "    fig = d2l.plt.imshow(img.asnumpy())\n",
    "    for row in output:\n",
    "        score = float(row[1])\n",
    "        if score < threshold:\n",
    "            continue\n",
    "        h, w = img.shape[0:2]\n",
    "        bbox = [row[2:6] * np.array((w, h, w, h), ctx=row.ctx)]\n",
    "        d2l.show_bboxes(fig.axes, bbox, '%.2f' % score, 'w')\n",
    "    d2l.plt.savefig('detected_image.jpg')\n",
    "display(img, output, threshold=0.5)"
   ]
  },
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   },
   "source": [
    "## 引用\n",
    "\n",
    "[1] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//European conference on computer vision. Springer, Cham, 2016: 21-37.<br />"
   ]
  }
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